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A novel motion control system for robotic platforms uses laser-ranging and graph traversal algorithms for optimal pathfinding. The A* algorithm proved most efficient for agricultural tasks, achieving high accuracy in real-world conditions.

Keywords:
LiDARapplicationbypass algorithmindustrial horticulturemotion trajectorypoint cloudpositioningrobotic platform

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Area of Science:

  • Robotics
  • Agricultural Engineering
  • Computer Science

Background:

  • Robotic platforms require precise motion control for autonomous operations in agriculture.
  • Navigation in complex environments like orchards presents significant challenges due to varying conditions.

Purpose of the Study:

  • To develop and evaluate a motion control system for robotic platforms using laser-ranging and optimal pathfinding algorithms.
  • To determine the most effective algorithm and optimal operating parameters for robotic navigation in agricultural settings.

Main Methods:

  • Implementation of a motion control system integrating laser-ranging, graph traversal, and the A* pathfinding algorithm.
  • Development of real-time path planning software in Rviz and a C# application for navigation graph optimization.
  • Experimental validation in agricultural buildings and open fields, analyzing positioning accuracy under various conditions.

Main Results:

  • The A* algorithm was identified as the most efficient for robotic platform pathfinding in agricultural operations.
  • Field conditions significantly impact positioning accuracy; optimal parameters identified as 2.5 km/h speed, 109,600 lux illumination, and 0.5 m distance to trees.
  • LiDAR sensors demonstrated high positioning accuracy in 3m wide garden aisles with 1.5m inter-stem distance and 0.5m tree crown width.

Conclusions:

  • The developed motion control system, utilizing laser-ranging and the A* algorithm, enables autonomous robotic operations in intensive gardens with high precision (≤8.4 cm deviation).
  • The system's performance is robust across various lighting and speed conditions, meeting agrotechnical requirements for autonomous agricultural tasks.